Synthetic aperture radar (SAR) systems exploit the motion of antennas arranged on a moving platform to synthesize a large virtual aperture and, thus, achieve high resolution imaging. Each virtual array at different spatial location forms a baseline. A single pass (single baseline) SAR system is capable of imaging a 2-dimensional (2D) range-azimuth reflectivity of an area of interest without any elevation resolution. However, the 3-dimensional (3D) structure of the area, such as 3D terrain features, is not preserved.
The 2D image is essentially a projection of the 3D reflectivity space into the 2D range-azimuth imaging plane. This projection can cause several artifacts. For example, in layover artifacts, several terrain patches with different elevation angles are mapped in the same range-azimuth resolution cell, see Gini et al, “Layover solution in multibaseline SAR interferometry,” IEEE Trans. Antennas and propagation, vol. 38(4), pp. 1344-1356, October 2002.
In shadowing artifacts, certain areas are not visible to the SAR system because another structure is in the illumination path. These artifacts cannot be resolved by a single pass, even using interferometric SAR techniques.
With the launch of the TerraSAR-X and the COSMO-Skymed satellites, 3D imaging has become possible. Those systems exploit stacks of complex-valued SAR images from multiple passes, which are collected at different baselines and at different time, to form 3D images that capture the 3D location and motion information of scattering objects, see Fornaro et al, “Three-dimensional focusing with multipass SAR data,” IEEE Trans. Geoscience and Remote Sensing, vol. 41(3), pp. 507-517, March 2003.
As shown in FIG. 1, a conventional 3D SAR system for generating a 3D image using multiple baseline arrays of antennas 101 mounted on a single radar platform in a 3D elevation, range and azimuth space. The figure shows point scatterers 102 for different elevations.
FIG. 2 show a conventional 3D imaging process for the system of FIG. 1. Data 201 are acquired at each baseline (1, . . . , N) 101. 2D SAR imaging 210 is applied independently to each data 201 to construct 2D images (I1, I2, . . . , IN) 215. The images are registered and aligned 220, followed by 3D image reconstruction 230 to obtain a 3D image 240.
With the additional elevation dimension, the 3D image can separate multiple scatterers along elevation, even when the scatterers are present in the same range-azimuth location. However, 3D imagery requires several trade-offs. First, to acquire images at multiple baselines, the platform needs to perform several passes over the area of interest. This makes data collection time consuming and very expensive. Second, the elevation resolution is much worse than that of range and azimuth due to the small elevation aperture, which is known as a tight orbital tube, of modern SAR sensors, e.g., ≈500 m diameter.
The elevation resolution can be improved using compressive sensing (CS) based approaches, see Zhu et al. “Tomographic SAR inversion by L1-norm regularization—the compressive sensing approach,” IEEE Trans. Geoscience and Remote Sensing, vol. 48(10), pp. 3839-3846, October 2010. That CS approach uses multiple baselines, a single PRF of a single SAR platform. In that method, a 2D range-azimuth image is reconstructed for each baseline. Then, compressive sensing based method is used improve elevation resolution. That method only considers sparsity for each 2D range-azimuth pixel.